Pulling in data
## pull in jw hourly met
jw_hr <- read.table(
"C:/Users/sears/Documents/Research/CPF/Data_downloads/joewright_met_hr_20220227.dat",
sep = ",", header=TRUE, skip="1") %>%
slice(., -(1:2)) %>%
mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
mutate_if(is.character,as.numeric) %>%
mutate(site = 'jw')
## pull in tc hourly met
tc_hr <- read.table(
"C:/Users/sears/Documents/Research/CPF/Data_downloads/tunnelcreek_met2_hr_20220227.dat",
sep = ",", header=TRUE, skip="1")%>%
slice(., -(1:2)) %>%
mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
mutate_if(is.character,as.numeric) %>%
mutate(site = 'tc')
## pull in mc hourly met
mc_hr <- read.csv("C:/Users/sears/Documents/Research/CPF/Data_downloads/mtncampus_20220223.csv") %>%
mutate(TIMESTAMP = ymd_hm(TIMESTAMP)) %>%
mutate(hour = hour(TIMESTAMP),
yday = yday(TIMESTAMP),
year = year(TIMESTAMP)) %>%
group_by(year, yday, hour) %>%
summarize_all(list(mean)) %>%
mutate(TIMESTAMP = floor_date(TIMESTAMP, "hour")) %>%
mutate(site = 'mc')
## pull in persistent burn site
bf_hr <- read.table(
"C:/Users/sears/Documents/Research/CPF/Data_downloads/wyatt/CR1000XSeries - new_Hourly_BF_2021.dat",
sep = ",", header=TRUE, skip="1") %>%
slice(., -(1:2)) %>%
mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
mutate_if(is.character,as.numeric) %>%
mutate(site = 'bf')
ub1_hr <- read.table(
"C:/Users/sears/Documents/Research/CPF/Data_downloads/wyatt/CR1000_7_One_Hour_UB_0110.dat",
sep = ",", header=TRUE, skip="1") %>%
slice(., -(1:2)) %>%
mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
mutate_if(is.character,as.numeric) %>%
mutate(site = 'ub')
ub2_hr <- read.table(
"C:/Users/sears/Documents/Research/CPF/Data_downloads/wyatt/CR1000_7_One_Hour_UB_0221.dat",
sep = ",", header=TRUE, skip="1") %>%
slice(., -(1:2)) %>%
mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
mutate_if(is.character,as.numeric) %>%
mutate(site = 'ub')
ub_hr <- bind_rows(ub1_hr, ub2_hr)
Binding data together
## compare incoming SW between sites, put in 1 df -- HOURLY
all_rad <- bind_rows(mc_hr, tc_hr, ub_hr, bf_hr, jw_hr) %>%
select(c(TIMESTAMP, SWin_Avg, SWout_Avg,
LWin_Avg, LWout_Avg,
SWalbedo_Avg, site)) %>%
filter(TIMESTAMP > ymd_hms("2021-11-01 00:00:00")) %>%
mutate(yday = yday(TIMESTAMP),
date = as_date(TIMESTAMP))
## Adding missing grouping variables: `year`, `yday`
## get into daily sums
all_rad_daily <- all_rad %>%
select(-TIMESTAMP, SWalbedo_Avg) %>%
group_by(date, site) %>%
summarize_all(list(sum))
Incoming shorwave rad
## plot SW in 1 plot
swin <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWin_Avg, color = site)) +
geom_line() +
ggtitle("SWin hourly")
ggplotly(swin)
swin_daily<- ggplot(all_rad_daily, aes(x = date, y= SWin_Avg, color = site)) +
geom_line() +
ggtitle("SWin daily sum")
ggplotly(swin_daily)
Outgoing shortwave rad
## SW out as one plot
swout <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWout_Avg, color = site)) +
geom_line() +
ggtitle("SWout hourly")
ggplotly(swout)
swout_daily<- ggplot(all_rad_daily, aes(x = date, y= SWout_Avg, color = site)) +
geom_line() +
ggtitle("SWout daily sum")
ggplotly(swout_daily)
Albedo
## plot albebo in 1 plot but limit the y vals
al <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWalbedo_Avg, color = site)) +
geom_line() +
ylim(0,1) +
ggtitle("Albedo hourly")
ggplotly(al)
#get an albedo daily avg
albedo_dailyavg <- all_rad %>%
select(date, site, SWalbedo_Avg) %>%
group_by(date, site) %>%
summarize(SWalbedo_daily = mean(SWalbedo_Avg))
## Adding missing grouping variables: `year`, `yday`
## `summarise()` has grouped output by 'date'. You can override using the
## `.groups` argument.
## daily albedo avg plot
al_daily <- ggplot(albedo_dailyavg, aes(x = date, y= SWalbedo_daily, color = site)) +
geom_line() +
ylim(0,1) +
ggtitle("Albedo daily average")
ggplotly(al_daily)
Net shortwave (computing SWin - SWout)
swnet <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWin_Avg-SWout_Avg, color=site)) +
geom_line() +
ggtitle("SWnet hourly")
ggplotly(swnet)
swnet_daily<- ggplot(all_rad_daily, aes(x = date, y= SWin_Avg-SWout_Avg, color = site)) +
geom_line() +
ggtitle("SWnet daily sum")
ggplotly(swnet_daily)